Abstract

We propose a method to recover the shape of a 3D room
from a full-view indoor panorama. Our algorithm can automatically
infer a 3D shape from a collection of partially
oriented superpixel facets and line segments. The core part
of the algorithm is a constraint graph, which includes lines
and superpixels as vertices, and encodes their geometric
relations as edges. A novel approach is proposed to perform
3D reconstruction based on the constraint graph by
solving all the geometric constraints as constrained linear
least-squares. The selected constraints used for reconstruction
are identified using an occlusion detection method with
a Markov random field. Experiments show that our method
can recover room shapes that can not be addressed by previous
approaches. Our method is also efficient, that is, the
inference time for each panorama is less than one minute.